PhD Studentship: Bayesian Modeling of High-dimensional Structural Data

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Organisation The University of Manchester

Organisation The University of Manchester Locations

Manchester, UK

Final date to receive applications 92 days remaining

We recommend that you apply early as the advert may be removed before the deadline.

In many applications such as biological sciences, social science, and engineering, we encounter high-dimensional observations. Bayesian approach can provide a flexible modeling framework for underlying structures in high dimension such as underlying covariance structure, conditional dependency graphs etc. With the change in data-generating mechanism, these high-dimensional structures may change with time, where the change can depend on latent factors or variables.

These projects will focus on developing a comprehensive Bayesian learning framework for this broad class of problems while focusing on specific applications. The goal would be to develop computationally efficient and scalable Bayesian learning methodologies with practical applications and establish relevant theoretical properties.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master's (or international equivalent) in a relevant science or engineering related discipline.

The National Institute of Teaching (NIoT)

Hybrid - Birmingham, Bristol, London, Blackburn, Redcar or Doncaster

Department for Environment, Food and Rural Affairs